Predictive modeling refers to generating a model from a given set of data records of both input parameters and output parameters and predicting actual output parameters corresponding to actual input parameters based on the model. Predictive models may be built by using various methods from data for many different families of models, such as decision trees, decision lists, linear equations, and neural networks.
The data records used to build a model are known as training data records. In certain situations, the training data records may be unable to cover the entire input space of the input parameters or the training data records may be discrete such that uniform relationships represented by a single predictive model between input parameters and output parameters may be unavailable across the entire input space and/or output space.
Techniques such as boosting and/or bagging may be used to divide the input space and/or output space by applying a large number of mathematical models. Each mathematical model may only cover a part of the input space and/or output space. For example, U.S. Pat. No. 6,546,379 (the '379 patent) issued to Hong et al. on Apr. 8, 2003, discloses a cascade boosting method for boosting predictive models for resolving the interpretability problem of previous boosting methods and mitigating the fragmentation problem when applied to decision trees.
However, such conventional techniques, while involving a large number of models, may cause coarse transitions from the large number of models. These coarse transitions may reduce the accuracy of the overall predictive model and may also cause confusion for the users of the overall predictive model.
Methods and systems consistent with certain features of the disclosed systems are directed to solving one or more of the problems set forth above.